Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

In Acta psychiatrica Scandinavica

OBJECTIVE : Promptly establishing maintenance therapy could reduce morbidity and mortality in patients with bipolar disorder. Using a machine learning approach, we sought to evaluate whether lithium responsiveness (LR) is predictable using clinical markers.

METHODS : Our data are the largest existing sample of direct interview-based clinical data from lithium treated patients (n=1266, 34.7% responders), collected across 7 sites, internationally. We trained a random forest model to classify LR-as defined by the previously validated Alda scale-against 180 clinical predictors.

RESULTS : Under appropriate cross-validation procedures, LR was predictable in the pooled sample with an area under the receiver operating characteristic curve of 0.80 (95% CI 0.78-0.82) and a Cohen's kappa of 0.46 (0.4-0.51). The model demonstrated a particularly low false positive rate (specificity 0.91 [0.88-0.92]). Features related to clinical course and the absence of rapid cycling appeared consistently informative.

CONCLUSION : Clinical data can inform out-of-sample LR prediction to a potentially clinically relevant degree. Despite the relevance of clinical course and the absence of rapid cycling, there was substantial between-site heterogeneity with respect to feature importance. Future work must focus on improving classification of true positives, better characterizing between- and within-site heterogeneity, and further testing such models on new external datasets.

Nunes Abraham, Ardau Raffaella, Berghöfer Anne, Bocchetta Alberto, Chillotti Caterina, Deiana Valeria, Garnham Julie, Grof Eva, Hajek Tomas, Manchia Mirko, Müller-Oerlinghausen Bruno, Pinna Marco, Pisanu Claudia, O’Donovan Claire, Severino Giovanni, Slaney Claire, Suwalska Aleksandra, Zvolsky Petr, Cervantes Pablo, Del Zompo Maria, Grof Paul, Rybakowski Janusz, Tondo Leonardo, Trappenberg Thomas, Alda Martin

2019-Oct-30

Lithium response, bipolar disorder, clinical prediction, machine learning